Industrial Use-case Research Articles

Efficient data management tools for the heterogeneous big data warehouse

The traditional RDBMS has been consistent for the normalized data structures. RDBMS served well for decades, but the technology is not optimal for data processing and analysis in data intensive fields like social networks, oil-gas industry, experiments at the Large Hadron Collider, etc. Several challenges have been raised recently on the scalability of data warehouse like workload against the transactional schema, in particular for the analysis of archived data or the aggregation of data for summary and accounting purposes. The paper evaluates new database technologies like HBase, Cassandra, and MongoDB commonly referred as NoSQL databases for handling messy, varied and large amount of data. The evaluation depends upon the performance, throughput and scalability of the above technologies for several scientific and industrial use-cases. This paper outlines the technologies and architectures needed for processing Big Data, as well as the description of the back-end application that implements data migration from RDBMS to NoSQL data warehouse, NoSQL database organization and how it could be useful for further data analytics.

Method for Selecting Improvement Measures for Discrete Production Environments Using an Extended Energy Value Stream Model

Improving energy efficiency in discrete manufacturing environments has been one of the key goals of recent research and industrial activities in production management, leading to a large and still increasing amount of different methodologies and improvement measures in that field (here called “Solution Elements”). Despite the growing interest, most of these are rarely applied, or are implemented in an improper manner instead of identifying and realizing the most appropriate ones from a holistic, factory wide perspective. For coping with these challenges a flexible, multi criteria and multi step selection process is introduced by which the applicability of solution elements is evaluated for a specific discrete production environment. The selection process is based on an approach taking multiple criteria derived from the current (as-is) situation in a factory as well as from user preferences into account. For data gathering an extended energy value stream approach is utilized that allows the derivation of value stream specific energy and cost drivers. The approach was tested in an industrial use-case, which is introduced in this paper.

A Case Study on Using a Proto-Application as a Proxy for Code Modernization

Abstract To take full advantage of future HPC systems, hybrid parallelization strategies are required. In a previous work, we demonstrated the Divide and Conquer, D&C, approach for efficient parallelization of finite element methods on unstructured meshes. In this paper we experiment the concept of proto-application as a proxy between computer scientists and application de- velopers on a real industrial use-case. The D&C library has been entirely developed on the proto-application and then validated on the original application. We also ported the D&C library to another fluid dynamic application, AETHER, developed by Dassault Aviation. The results show that the speed-up validated on the proto-application can be reproduced on other full scale applications using similar computational patterns.

A random-key encoded harmony search approach for energy-efficient production scheduling with shared resources

When seeking near-optimal solutions for complex scheduling problems, meta-heuristics demonstrate good performance with affordable computational effort. This has resulted in a gravitation towards these approaches when researching industrial use-cases such as energy-efficient production planning. However, much of the previous research makes assumptions about softer constraints that affect planning strategies and about how human planners interact with the algorithm in a live production environment. This article describes a job-shop problem that focuses on minimizing energy consumption across a production facility of shared resources. The application scenario is based on real facilities made available by the Irish Center for Manufacturing Research. The formulated problem is tackled via harmony search heuristics with random keys encoding. Simulation results are compared to a genetic algorithm, a simulated annealing approach and a first-come-first-served scheduling. The superior performance obtained by the proposed scheduler paves the way towards its practical implementation over industrial production chains.

A Simple Data-Muling Protocol

More and more industrial infrastructures consider the use of industrial wireless sensor network (IWSN) technology to perform control activities in industry. The benefits stemmed from IWSNs include flexible access (no wires are required) to elements that may interact with the factory floor. However, this support introduces new challenges: muling the almost permanent connectivity given by wired infrastructures is no longer available in IWSNs. To address this challenge, this paper explores data muling as the ability of transferring data between different elements within an industrial infrastructure. The proposed data-muling protocol uses a dual discovery and update (D&U) strategy. The prototype evaluation carried out on 6LowPan over IEEE 802.15.4 motes provides some insights on the performance that this type of infrastructure may offer within an industrial context. This paper also offers an extensive benchmark that may be used to test bounds and limitations of the proposed D&U protocol on the industrial use-cases.

Input variable selection in time-critical knowledge integration applications: A review, analysis, and recommendation paper

The purpose of this research is twofold: first, to undertake a thorough appraisal of existing Input Variable Selection (IVS) methods within the context of time-critical and computation resource-limited dimensionality reduction problems; second, to demonstrate improvements to, and the application of, a recently proposed time-critical sensitivity analysis method called EventTracker to an environment science industrial use-case, i.e., sub-surface drilling.Producing time-critical accurate knowledge about the state of a system (effect) under computational and data acquisition (cause) constraints is a major challenge, especially if the knowledge required is critical to the system operation where the safety of operators or integrity of costly equipment is at stake. Understanding and interpreting, a chain of interrelated events, predicted or unpredicted, that may or may not result in a specific state of the system, is the core challenge of this research. The main objective is then to identify which set of input data signals has a significant impact on the set of system state information (i.e. output). Through a cause-effect analysis technique, the proposed technique supports the filtering of unsolicited data that can otherwise clog up the communication and computational capabilities of a standard supervisory control and data acquisition system.The paper analyzes the performance of input variable selection techniques from a series of perspectives. It then expands the categorization and assessment of sensitivity analysis methods in a structured framework that takes into account the relationship between inputs and outputs, the nature of their time series, and the computational effort required. The outcome of this analysis is that established methods have a limited suitability for use by time-critical variable selection applications. By way of a geological drilling monitoring scenario, the suitability of the proposed EventTracker Sensitivity Analysis method for use in high volume and time critical input variable selection problems is demonstrated.

Model driven code generation for critical and adaptative embedded systems

Implementing adaptative systems requires to make the trade off of (i) the adaptation promptness, (ii) the amount of interferences due to adaptation, and (iii) the guarantee of data ows consistency. Furthermore, implementing critical systems requires to respect design constraints that enable to improve its determinism. In this paper, we propose a design methodology that helps to make the adaptation trade off while respecting the critical systems design specificities. We then illustrate the usage of this methodology on an industrial use-case, using a component-based framework that generates the adaptation code. Lastly, we present and discuss the associated experimental results, and show the relative benefits of using the one or the other mode switch protocol.

Sampled-data control of hybrid systems with discrete inputs and outputs

We address the control synthesis of hybrid systems with discrete inputs, disturbances and outputs. The control objective is to ensure that the events of the closed-loop system belong to the language of the control requirements. The controller is sampling-based and it is representable by a finite-state machine. We formalize the control problem and provide a theoretically sound solution. The solution is based on solving a discrete-event control problem for a finite-state abstraction of the plant. We propose a specific construction for the finite-state abstraction. This construction is not based on discretizing the state-space, but rather on converting the continuous-time hybrid system to a discrete-time one based on sampling. The construction works only for a specific class of hybrid systems. We describe this class of systems and we provide an example of such a system, inspired by an industrial use-case.